This SBIR aims to produce commercial software for handling missing data in large data sets, where the goal is data mining and knowledge discovery. There may be a large number of subjects, variables, or both. Examples include microarray data, surveys, genomic data, and high throughput screening data. Handling missing data is one important step of careful data preparation, which is key to the success of an entire project. Missing values often arise in medical data. This is an obstacle because many data mining tools either require complete data or are not robust to missing data. Principled methods of handling missing data are computationally intensive. Therefore computational feasibility is a challenge to handling missing values in large data sets. Phase I work will explore strategies such as sampling, constraining parameters, and monotone data algorithms for model based techniques. Factor analysis and multivariate linear mixed effects models will be used to reduce the number of parameters. A variable-by-variable approach using a popular data mining technique, recursive partitioning, will also be used to impute missing values. For each of the methods, we will write prototype software and test performance on missing data patterns simulated on real data. Several ad hoc techniques will serve as a baseline for comparison. Experience writing prototypes and using them in simulations will lead to preliminary software design that will serve as the foundation of Phase II work. This proposed software will enable medical researchers to gain more from their data mining efforts: maximally extracting information and achieving unbiased predictions, despite missing data.